• J. Surg. Res. · Jun 2016

    Clinical Trial

    A noncontact RF-based respiratory sensor: results of a clinical trial.

    • Spence Madsen, Jordan Baczuk, Kurt Thorup, Richard Barton, Neal Patwari, and John T Langell.
    • Department of Electrical Engineering, University of Utah, Salt Lake City, Utah.
    • J. Surg. Res. 2016 Jun 1; 203 (1): 1-5.

    BackgroundRespiratory rate (RR) is a critical vital signs monitored in health care setting. Current monitors suffer from sensor-contact failure, inaccurate data, and limited patient mobility. There is a critical need for an accurate and reliable and noncontact system to monitor RR. We developed a contact-free radio frequency (RF)-based system that measures movement using WiFi signal diffraction, which is converted into interpretable data using a Fourier transform. Here, we investigate the system's ability to measure fine movements associated with human respiration.Materials And MethodsTesting was conducted on subjects using visual cue, fixed-tempo instruction to breath at standard RRs. Blinded instruction-based RRs were compared to RF-acquired data to determine measurement accuracy. The RF-based technology was studied on postoperative ventilator-dependent patients. Blinded ventilator capnographic RR data were collected for each patient and compared to RF-acquired data to determine measurement accuracy.ResultsRespiratory rate data collected from 10 subjects breathing at a fixed RR (14, 16, 18, or 20) demonstrated 95.5% measurement accuracy between the patient's actual rate and that measured by our RF technology. Ten patients were enrolled into the clinical trial. Blinded ventilator capnographic RR data were compared to RF-based acquired data. The RF-based data showed 88.8% measurement accuracy with ventilator capnography.ConclusionsInitial clinical pilot trials with our contact-free RF-based monitoring system demonstrate a high degree of RR measurement accuracy when compared to capnographic data. Based on these results, we believe RF-based systems present a promising noninvasive, inexpensive, and accurate tool for continuous RR monitoring.Copyright © 2016 Elsevier Inc. All rights reserved.

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